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A meta-heuristic satisfying tradeoff method for solving multiobjective combinatorial optimization problems-with application to flowshop scheduling

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4 Author(s)
I. Tamura ; Dept. of Syst. & Human Sci., Osaka Univ., Japan ; T. Shibata ; S. Tomiyama ; I. Hatono

In this paper an effective meta-heuristic approach is proposed to realize a satisfying tradeoff method for solving multiobjective combinatorial optimization problems. Firstly, Pareto optimal solutions (individuals) are generated by using a genetic algorithm with the family elitist concept for a multiobjective combinatorial optimization problem. Then, we try to find a preferred solution of the decision maker based on the satisfying tradeoff method. In this paper a new meta-heuristic satisfying tradeoff method is proposed in which we do not need to solve a complex min-max problem in each iteration, but we try to find a min-max solution in the Pareto optimal solutions (individuals) generated by the genetic algorithm. We further revise the min-max solution by using a local search approach such as a simulated annealing method. As a numerical example a flowshop scheduling problem is included to verify the effectiveness of the method proposed in this paper

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Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on  (Volume:3 )

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